Predictive test for selection of metastatic breast cancer patients for hormonal and combination therapy

ABSTRACT

A mass-spectral method is disclosed for determining whether a breast cancer patient is likely to benefit from administration of a combination treatment in the form of a targeted anti-cancer drug in addition to an endocrine therapy drug. The method obtains a mass spectrum from a blood-based sample from the patient. The spectrum is subject to one or more predefined pre-processing steps. Values of selected features in the spectrum at one or more predefined m/z ranges are obtained. The values are used in a classification algorithm using a training set comprising class-labeled spectra a class label for the sample is obtained. If the class label is “Poor”, the patient is identified as being likely to benefit from the combination treatment. In a variation, the “Poor” class label predicts whether the patient is unlikely to benefit from endocrine therapy drugs alone, regardless of the patient&#39;s HER2 status.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority benefits under 35 U.S.C. §119(e) toU.S. Provisional application Ser. No. 61/437,575 filed Jan. 28, 2011,the contents of which are incorporated by reference herein.

BACKGROUND

This application relates generally to the field of treatment of breastcancer and more particularly to a predictive test for determining, inadvance of treatment, whether a breast cancer patient is a member of aclass of patients that would be likely to benefit from a combination ofcertain anti-cancer drugs. The application also relates to a predictivetest for determining, in advance of treatment, whether a breast cancerpatient is a member of a class of patients that would not be likely tobenefit from endocrine therapy alone, including for example an aromataseinhibitor such as letrozole.

The applicant's Assignee Biodesix, Inc. has developed a predictive testfor determining whether certain cancer patients would be likely tobenefit from anti-cancer drugs or combinations of drugs. The commercialversion of the test, known as VERISTRAT®, is a MALDI-ToF massspectrometry serum-based test that has clinical utility in the selectionof specific targeted therapies in solid epithelial tumors. See U.S. Pat.No. 7,736,905, the content of which is incorporated by reference herein,which describes the test in detail. In brief, a mass spectrum of a serumsample of a patient is obtained. After certain pre-processing steps areperformed on the spectrum, the spectrum is compared with a training setof class-labeled spectra of other cancer patients with the aid of aclassifier. The class-labeled spectra are associated with two classes ofpatients: those that benefitted from epidermal growth factor receptorinhibitors (EGFRIs), class label of “Good”, and those that did not,class label of “Poor”. The classifier assigns a class label to thespectrum under test. The class label for the sample under test is either“Good” or “Poor,” or in rare cases where the classification test failsthe class label for the sample is deemed “undefined.”

Patients whose sample is identified by the test as Poor are identifiedas members of a group or class of patients which appear to be unlikelyto obtain clinical benefit from treatment with epidermal growth factorreceptor inhibitors (EGFRIs) such as gefitinib (Iressa®), erlotinib(Tarceva®), and cetuximab (Erbitux®) in the treatment of solidepithelial tumors. The complementary patient population, associated withthe class label of Good, is likely to benefit depending on the detailsof the indication. In the absence of treatment, the VeriStrat test has astrong prognostic component, meaning that “Poor” patients performsignificantly worse than “Good” patients.

The VeriStrat Poor signature has been found in a variety of solid tumorsincluding non-small cell lung cancer (NSCLC), colorectal cancer (CRC),and squamous cell cancer of the head and neck (SCCHN or, alternatively,H&N). The following patents documents of the applicant's assigneedescribe further background information concerning the VeriStrat testand its applications: U.S. Pat. Nos. 8,024,282; 7,906,342; 7,879,620;7,867,775; 7,858,390; 7,858,389 and 7,736,905.

Breast cancer is the leading form of cancer in women and the secondleading cause of cancer death in women, after lung cancer. Thedevelopment of breast cancer is believed to be a multi-step process ofgenetic alteration that transforms normal cells into highly malignantderivatives.

It has been known for many years that changing the hormonal balance of apatient with breast cancer could lead to changes in tumor growth andregression of metastatic disease. Estrogen in particular can promote thegrowth of breast cancer cells. Accordingly, while treatment of breastcancer can follow several avenues, including surgery and chemotherapy,so-called endocrine therapies that are designed to block the generationor uptake of estrogen are commonly used in treatment of breast cancer.See generally A. Goldhirsch et al.[1]. Currently, one of the mostpromising avenues of endocrine therapy takes the form of administrationof drugs that modulate estrogen synthesis and inhibit estrogen receptorpathways.

Agents targeting estrogen receptors include selective estrogen receptormodulators (SERMs) and selective estrogen receptor downregulators(SERDs). Both SERDs and SERMs are in use in treatment of breast cancer.Tamoxifen, a most often used agent in pre-menopausal setting, is anestrogen receptor antagonist in breast tissue, but acts as an agonist insome other tissues, hence it belongs to the SERM class. Inpost-menopausal women tamoxifen is also used, as well as some otherantagonists, such as Fulvestrant (a SERD) and toremifene (a SERM).Tamoxifen, a non-steroidal antiestrogen, is thought to inhibit breastcancer growth by competitively blocking estrogen receptor (ER), therebyinhibiting estrogen-induced growth. ER is a ligand-dependenttranscription factor activated by estrogen. Upon interaction with thehormone it enters the nucleus, binds to specific DNA sequences andactivates ER-regulated genes, mediating most biological effects ofestrogens on normal cells and estrogen-dependent tumors.

Endocrine therapy drugs also include a class of drugs known as aromataseinhibitors, including selective and nonselective aromatase inhibitors.Selective aromatase inhibitors include letrozole, as well as anastrozole(arimidex); another similar acting, however non-reversible, agent isexemestane (aromasin). Aromatase is an enzyme that synthesizes estrogenin the body by converting the hormone androgen into estrogen. Aromataseinhibitors stop the production of estrogen by blocking the aromatase.Administration of aromatase inhibitors thus reduces the amount ofestrogen which is available to stimulate the growth of hormonereceptor-positive breast cancer cells. In post-menopausal settingsletrozole, anastrozole, and exemestane are aromatase inhibitors (AIs)that are used most frequently.

Many breast cancer patients have a primary resistance or develop tumorresistance to endocrine therapy despite detected hormone receptor(HR)-positive status. The art has recognized a variety of methods forattempting to predict resistance to endocrine therapy in breast cancerpatients. See U.S. Pat. Nos. 7,217,533; 7,642,050; 7,504, 214;7,402,402; 7,537,891, 7,504,211; 5,693,463 and the article of Ma et al[2]. These methods typically involve either determining whether breastcancer cells express certain gene expression products or profiles, oranalyzing certain ratios of certain gene expression products.

SUMMARY

Up to 50% of women with breast cancers that are hormonereceptor-positive do not derive benefit from endocrine therapymodulating tumor estrogen receptor function or reducing the level ofcirculation estrogens. [2]

We have discovered a method for determining whether a hormone receptorpositive breast cancer patient, regardless of their HER2 status, isunlikely to benefit from administration of an endocrine therapy drugalone for treatment of the cancer. Unlike the prior art, our methodsinvolve a mass-spectrometry test that uses a blood-based sample from thepatient (serum or plasma) to make this determination. This methodinvolves a) obtaining a mass spectrum from a blood-based sample from thepatient; b) performing one or more predefined pre-processing steps onthe mass spectrum obtained in step a); c) obtaining values of selectedfeatures in the mass spectrum at one or more predefined m/z ranges afterthe pre-processing steps on the mass spectrum in step b) have beenperformed; and d) using the values obtained in step c) in aclassification algorithm using a training set comprising class-labeledspectra produced from samples from other cancer patients and obtaining aclass label for the sample. The class label assigned to the massspectrum by the classification algorithm predicts whether the breastcancer patient is likely to benefit. In particular, if the class labelobtained in step d) is “Poor” or the equivalent, the patient isidentified as being unlikely to benefit from the endocrine therapy drug.

In another aspect, we have discovered a mass-spectrometry test or methodof determining whether a post-menopausal hormone receptor positivebreast cancer patient with HER2 negative status is likely to benefitfrom administration of a combination treatment comprising administrationof a targeted anti-cancer drug in addition to an endocrine therapy drug.Our method includes the steps of a) obtaining a mass spectrum from ablood-based sample from the patient; b) performing one or morepredefined pre-processing steps on the mass spectrum obtained in stepa); c) obtaining values of selected features in said spectrum at one ormore predefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed; and d) using the valuesobtained in step c) in a classification algorithm using a training setcomprising class-labeled spectra produced from samples from other cancerpatients and obtaining a class label for the sample. If the class labelobtained in step d) is “Poor” or the equivalent then the patient isidentified as being likely to benefit from the combination treatment.

We have arrived at these methods by examining the effect of separationof blood-based samples using the VeriStrat test on the treatmentefficacy of letrozole with or without lapatinib in first line metastaticbreast cancer patients in a retrospective analysis of the phase IIItrial EGF30008 [3]. The total number of patients in this trial was 1258,1164 pretreatment serum samples were available for analysis. We obtainedvalid VeriStrat test results for 1046 of these patients, of which 961were classified as VeriStrat Good, 80 were VeriStrat Poor, and 5 wereVeriStrat Indeterminate (Undefined) (patients for whom 3 replicatespectra produced discrepant results); 117 samples were not evaluable dueto hemolysis and we could not assign a VeriStrat label to one patientdue to data inconsistencies between available samples.

As a result of the analysis of EGF30008 we have made severalobservations leading to the present inventive methods. One of which isthat, for those patients having a mass spectral signature that isclassified as “poor” using the training set, that class label identifiesthose patients that are not likely to benefit from administration ofendocrine therapy alone, regardless of their HER2 status. Such patientscan be characterized as “endocrine resistant”, i.e., resistant toendocrine therapy drugs. Patients with hormone-receptor positive statusare considered to be sensitive to endocrine therapy, however up to40-50% of them do not respond to it from the beginning of treatment orstop responding at some point in the course of treatment. That's why ourfinding that we can identify asubset not benefiting from endocrinetherapy (despite being hormone receptor-positive) is an importantresult. Since the patient is predicted to not benefit in advance oftreatment, the patient can be steered into the direction of othertreatments that are more likely to lead to a favorable outcome from thestart.

In pre-menopausal women estrogen is produced mainly in the ovaries,hence, the treatment strategy for the HR-positive breast cancer in thispopulation involves ovarian suppression usually in combination with ERmodulator, tamoxifen. In post-menopausal women ovarian function hasceased and estrogen is synthesized in smaller quantities from androgens.Aromatase plays a key role in this process, providing a biologicalrationale for using aromatase inhibitors (AIs) for treatment ofHR-positive breast cancer in post-menopausal women. Both ER modulators(tamoxifen) and aromatase inhibitors show effectiveness inpost-menopausal women. Recent publications provide conflicting advice onthe role of AIs in the treatment of postmenopausal patients withearly-stage hormone receptor-positive breast cancer. On one hand,Chlebowski [4] recommends up-front AI for the majority of patients,whereas Seruga and Tannock [5] suggest that tamoxifen remains theendocrine treatment of choice for most patients. Meta-analysis of breastcancer outcomes in adjuvant trials of aromatase inhibitors versustamoxifen in post-menopausal women showed that AIs produce significantlylower recurrence rates compared with tamoxifen, either as initialmonotherapy or after 2 to 3 years of tamoxifen. At 5 years, AI therapywas associated with an absolute 2.9% decrease in recurrence and anonsignificant absolute 1.1% decrease in breast cancer mortality[6]. TheATAC trial of Anastrozole, Tamoxifen, Alone or in Combination showedthat 5 years of treatment with anastrozole was generally bettertolerated than 5 years of treatment with tamoxifen, and led to lowerrecurrence rates, especially in hormone receptor-positive women (26%reduction), however the benefits on late end points, such as distantrecurrence and death after recurrence, were marginal[7]. Comparisons ofvarious Ms in randomized clinical trials show that while there is somedifference in the outcomes, it is often difficult to choose between theagents. For example in a comparative trial of aromatase inhibitorsletrozole and anastrozole, letrozole was significantly superior toanastrozole in the overall response rate (ORR), however there were nosignificant differences between the treatment arms in the rate ofclinical benefit, median duration of response, duration of clinicalbenefit, time to treatment failure, or overall survival.[8] Similarityof the mechanisms of action as well as of clinical outcomes in clinicaltrials with different AIs give us a reason to expect that separation ofbreast cancer patients by VeriStrat test with respect to clinicalbenefit observed with letrozole is likely to be similar to other AIs. Inaddition, taking into consideration that both tamoxifen's and AIs'therapeutic effects are based on the reduction of activated hormone—ERreceptor complexes in the cell, either through the inhibition ofestrogen synthesis or minimization of number of receptors available forligand binding, one can hypothesize that the effect observed in thestudy with one of AIs (letrozole) is likely to be similar in the case oftreatment with an estrogen modulator tamoxifen. Hence, the VeriStrattest may be of significant clinical utility in various types of hormonaltherapy of breast cancer.

Furthermore, we have observed that the addition of lapatinib toletrozole significantly improves patient outcome in the “Poor” group buthas little or no clinical benefit in the “Good” group. This observationholds even under further stratification into HER2positive (HER2+) andHER2-negative (HER2-)strata. Our mass-spectrometry test was shown to bea predictive test for the benefit of adding lapatinib to letrozoletreatment as the p-value of treatment*VeriStrat status interaction wasfound to be significant, and this significance remained even inmultivariate analysis adjusted for possible confounding factors. Whileit is not surprising that there is benefit from adding lapatinib toletrozole in HER2+ patients, the observation that adding lapatinib toletrozole in HER2− patients can lead to a substantial improvement inprogression free survival in a selected population is unexpected. Thispatient subgroup can be identified by a mass-spectrometry test conductedon a blood-based sample in advance of treatment. This patient selectionprocess may lead to improved treatment paradigms. For example, one couldtest all hormone receptor positive patients for VeriStrat status (i.e.,class label “Good” or “Poor”); if a patient were classified as Poor theywould be likely to benefit from the addition of lapatinib to letrozoleirrespective of the patient's HER2 status. If a patient's VeriStratstatus were VeriStrat Good, one could then perform a HER2 test to decidewhether the addition of lapatinib would be appropriate. Alternatively,if the HER2 status were known to be HER2-negative, one can perform theVeriStrat test to decide whether the patient belongs to the VeriStratPoor subgroup and may benefit from the addition of lapatinib.

Given the result obtained with the dual HER2/EGFR inhibitor lapatinib,one can expect similar effects in VeriStrat Poor patients withHER2-negative status from the addition to letrozole of other agentstargeting HER2/EGFR, e.g neratinib, afatinib, or combinations of agentsaimed at the same receptors, e.g. erlotinib or gefitinib plustrastuzumab.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a mass-spectrometry based test forpredicting breast cancer patient response to certain drugs orcombination of drugs for use in the methods of this disclosure.

FIGS. 2-13 show data resulting from our retrospective analysis of thephase III EGF 30008 trial, and in particular:

FIG. 2 is a Kaplan-Meier plot of Progression Free Survival (PFS) foroverall population by VeriStrat classification and treatment arm. FIG. 2shows that patients have similar outcomes on the combination oflapatinib and letrozole regardless of their VeriStrat status, but not onletrozole alone. In particular, FIG. 2 shows that, for those patientstreated with letrozole alone, patients identified as “Poor” do muchworse on letrozole alone than those patients identified as “Good.” FIG.2 also shows that patients whose serum was classified as “Poor” showedimproved progression free survival (PFS) with the addition of lapatinibto letrozole.

FIG. 3 is a Kaplan-Meier plot of PFS for the letrozole arm by VeriStratclassification. FIG. 3 demonstrates that our VeriStrat test identifies agroup of patients with poor outcomes on letrozole alone.

FIG. 4 is a Kaplan-Meier plot of PFS for “Good” patients by treatmentarm.

FIG. 5 is a Kaplan-Meier plot of PFS for “Poor” patients by treatmentarm. FIG. 5 illustrates that patients whose serum is classified as“Poor” benefit significantly more with combination treatment (lapatiniband letrozole) than those receiving letrozole alone; the median PFS isgreater by 8.2 months with combination treatment. The significance ofthe difference in benefit is demonstrated in the multivariate analysiswith the interaction term included.

FIG. 6 is a Kaplan-Meier plot of PFS for by VeriStrat classification andtreatment arm for HER2− population.

FIG. 7 is a Kaplan-Meier plot of PFS for the letrozole arm by VeriStratclassification for HER2− patients.

FIG. 8 is a Kaplan-Meier plot of PFS for the letrozole arm by VeriStratclassification for HER2+ patients. FIGS. 7 and 8 show that our testidentifies patients with poor outcomes on letrozole alone, independentof HER2 status.

FIG. 9 is a Kaplan-Meier plot of PFS for VeriStrat Good patients bytreatment arm for HER2− patients.

FIG. 10 is a Kaplan-Meier plot of PFS for VeriStrat Poor patients bytreatment arm for HER2− patients. FIG. 10 demonstrates that HER2−patients whose serum is classified as “Poor” showed a trend for improvedPFS with the addition of lapatinib to letrozole as compared to treatmentby letrozole alone.

FIG. 11 is a Kaplan-Meier plot of PFS for HER2+ patients by VeriStratclassification and treatment arm. It shows that patients have similaroutcomes with lapatinib plus letrozole treatment regardless of theirVeriStrat classification.

FIG. 12 is a Kaplan-Meier plot of PFS for VeriStrat “Good” patients bytreatment arm for HER2+ patients.

FIG. 13 is a Kaplan-Meier plot of PFS for VeriStrat “Poor” patients bytreatment arm for HER2+ patients. FIGS. 11-13 demonstrate that, withinthe population of HER2+ patients, patients have similar outcomes withlapatinib plus letrozole regardless of their VeriStrat classification.

DETAILED DESCRIPTION

Our work leading to the present inventive methods involved evaluatingthe effect of VeriStrat separation (“Good” vs. “Poor”) on the treatmentefficacy of letrozole with or without lapatinib in first line metastaticbreast cancer patients in a retrospective analysis of the phase IIItrial EGF30008 (see S Johnston et al, [3] attached as an appendix to ourprior provisional application). Our work involved obtaining serumsamples from patients involved in this study, obtaining mass spectra ofsuch samples, and subjecting the spectra to a classifier we havedeveloped and described in our U.S. Pat. No. 7,736,905. The classifierassigned a class label to the samples, either “Good” or “Poor” or in afew instances “undefined.” The class labels were assigned using aK-nearest neighbor (KNN) scoring algorithm based on a comparison of thespectra, after preprocessing and calculation of integrated intensityvalues at selected features in the spectra, with a training set ofclass-labeled spectra from other cancer patients.

In the study we conducted, the training set used by the classificationalgorithm used class-labeled spectra from a population of non-small celllung cancer patients, with the class-label in the training set being“Good” if the associated spectra in the training set was assigned to apatient who benefitted from administration of an EGFR-I, whereas theclass label “Poor” was assigned to spectra for patients who did notbenefit from such drugs. This training set and the classifier was thesubject of extensive validation studies. The method of conducting ourmass-spectral testing and classification of blood-based samples isexplained in further detail below.

In our retrospective analysis of the EGF30008 study, we obtained serumsamples from the patients participating in the study, subjected them toour VeriStrat test, and the samples were assigned class labels of Goodor Poor, depending on the outcome of the KNN algorithm in theclassifier. We studied these class labels, along with clinical dataassociated with the patients in this study and made a number ofsurprising discoveries.

In particular, as a result of the analysis of EGF30008, and ourunderstanding of the drugs involved in this study, we have discoveredthat our mass spectral testing method provides the ability to identify acertain class of hormone receptor positive breast cancer patients thatare not likely to benefit from endocrine therapy drugs alone intreatment of the cancer. This class of patient is identified when theclassifier assigns the “Poor” class label to the sample's mass spectrum.

We also discovered that the addition of lapatinib to letrozolesignificantly improves patient outcome in the “Poor” group but haslittle or no clinical benefit for those patients identified as “Good”.This observation holds even under further stratification into HER2+ andHER2− strata. Our mass-spectral test was shown to be a predictive testfor the benefit of adding lapatinib to letrozole treatment as thep-value of treatment*VeriStrat status interaction was found to besignificant, and this significance remained even in multivariateanalysis adjusted for possible confounding factors.

While it is not surprising that there is benefit from adding lapatinibto letrozole in HER2+ patients, the observation that adding lapatinib toletrozole in HER2− patients can lead to a substantial improvement inprogression free survival in a selected population is unexpected. Thispatient subgroup can be identified by mass-spectrometry testing on aserum sample in advance of treatment, and this patient selection maylead to improved treatment paradigms. For example, one could test allhormone receptor positive patients for VeriStrat status; if a patientwere classified as Poor they would benefit from the addition oflapatinib to letrozole irrespective of the patient's HER2 status. If apatient's VeriStrat status were Good, then one could perform a HER2 testto decide whether the addition of lapatinib would be appropriate.Alternatively, if HER2 status is known, one can perform the VeriStrattest on HER2-negative patients and identify those (VeriStrat Poor)patients who would benefit from the addition of lapatinib

The discoveries resulting from our study of these samples and theVeriStrat testing can take the form of practical, useful tests. Oneaspect is that our testing method identifies a group of hormone receptorpositive breast cancer patients that are not likely to benefit fromadministration of an endocrine therapy drug alone. This identificationcan be made in advance of treatment.

In this first aspect, the method is described herein for determiningwhether a hormone receptor positive breast cancer patient, regardless ofthe patients' HER2 status, is unlikely to benefit from administration ofan endocrine therapy drug alone for treatment of the cancer. The methodincludes the steps of: a) obtaining a mass spectrum from a blood-basedsample from the patient; b) performing one or more predefinedpre-processing steps on the mass spectrum obtained in step a); c)obtaining values of selected features in the spectrum at one or morepredefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed; d) using the values obtained instep c) in a classification algorithm using a training set comprisingclass-labeled spectra produced from samples from other cancer patientsand obtaining a class label for the patient's sample; and e) if theclass label obtained in step d) is “Poor” or the equivalent, then thepatient is identified as being unlikely to benefit from the treatment.

Those patients that do not respond well to endocrine therapy drugs alonecan be said to be “endocrine resistant.” That is, patients withhormone-receptor positive status are considered to be sensitive toendocrine therapy, however some of them do not respond to it from thebeginning of treatment, while others can stop responding at some point.Our finding that we can identify the subset of patients that ispredicted to not benefit from endocrine therapy drugs (despite beinghormone receptor-positive), and that identification can be made inadvance of initiating treatment, is an important result.

A second practical test is described herein in the form of a method ofdetermining whether a post-menopausal hormone receptor positive breastcancer patient with HER2-negative status is likely to benefit fromadministration of a combination treatment comprising administration of atargeted anti-cancer drug in addition to an endocrine therapy drug. Themethod involves the steps of: a) obtaining a mass spectrum from ablood-based sample from the patient; b) performing one or morepredefined pre-processing steps on the mass spectrum obtained in stepa); c) obtaining values of selected features in said spectrum at one ormore predefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed; d) using the values obtained instep c) in a classification algorithm using a training set comprisingclass-labeled spectra produced from samples from other cancer patientsand obtaining a class label for the sample; and e) if the class labelobtained in step d) is “Poor” or the equivalent then the patient isidentified as being likely to benefit from the combination treatment.

Cross-talk between pathways involved with estrogen receptors and HER2and EGFR is considered as one of the primary mechanisms of thisresistance to letrozole alone and constitutes the rationale for thecombination of drugs (targeted therapies and endocrine therapies) usedin our study, since inhibition of production of estrogen and at the sametime of HER2 and EGFR signaling, stops these interactions and helps toprevent/overcome resistance. Here, the important finding we have made isthat patients that seemed to be resistant (non-benefiting) to letrozolealone respond to the combination of targeted therapies and endocrinetherapies (e.g., the combination of lapatinib plus aromatase inhibitorsuch as letrozole), and, most interestingly, in the HER2-negative groupas well if they are classified as “Poor” in our test. HER2-negativepatients were not expected to gain benefit from the combinationtreatment, but we have been able to identify a subgroup of HER2-negativepatients that are likely to benefit from the combination treatment,which is a significant advance.

The VeriStrat Test

The methods of this disclosure for identifying a set of hormone receptorpositive breast cancer patients that are not likely to benefit from anendocrine therapy (e.g., aromatase inhibitor, tamoxifen, other SERMs andSERDs) alone, or alternatively to benefit from the addition of certaintargeted therapies and endocrine therapy drugs, involves obtaining ablood-based sample (serum or plasma) of the breast cancer patient andprocessing it in accordance with the test described in this section ofthis document. The class label assigned to the specimen indicateswhether the patient is unlikely to benefit from the administration ofthe endocrine therapy drug alone, or alternatively likely to benefitfrom the administration of a combination of a targeted therapy and anendocrine therapy drug. The test is illustrated in flow chart form inFIG. 1 as a process 100.

At step 102, a serum or plasma sample is obtained from the patient. Inone embodiment, the serum samples are separated into three aliquots andthe mass spectroscopy and subsequent steps 104, 106 (including sub-steps108, 110 and 112), 114, 116 and 118 are performed independently on eachof the aliquots. The number of aliquots can vary, for example there maybe 4, 5 or 10 aliquots, and each aliquot is subject to the subsequentprocessing steps.

At step 104, the sample (aliquot) is subject to mass spectroscopy. Apreferred method of mass spectroscopy is matrix assisted laserdesorption ionization (MALDI) time of flight (TOF) mass spectroscopy,but other methods are possible. Mass spectroscopy produces data pointsthat represent intensity values at a multitude of mass/charge (m/z)values, as is conventional in the art. In one example embodiment, thesamples are thawed and centrifuged at 1500 rpm for five minutes at fourdegrees Celsius. Further, the serum samples may be diluted 1:10, or 1:5,in MilliQ water. Diluted samples may be spotted in randomly allocatedpositions on a MALDI plate in triplicate (i.e., on three different MALDItargets). After 0.75 ul of diluted serum is spotted on a MALDI plate,0.75 ul of 35 mg/ml sinapinic acid (in 50% acetonitrile and 0.1%trifluoroacetic acid (TFA)) may be added and mixed by pipetting up anddown five times. Plates may be allowed to dry at room temperature. Itshould be understood that other techniques and procedures may beutilized for preparing and processing serum in accordance with theprinciples of the present invention.

Mass spectra may be acquired for positive ions in linear mode using aVoyager DE-PRO or DE-STR MALDI TOF mass spectrometer with automated ormanual collection of the spectra. Seventy five or one hundred spectraare collected from seven or five positions within each MALDI spot inorder to generate an average of 525 or 500 spectra for each serumspecimen. Spectra are externally calibrated using a mixture of proteinstandards (Insulin (bovine), thioredoxin (E. coli), and Apomyglobin(equine)).

At step 106, the spectra obtained in step 104 are subject to one or morepre-defined pre-processing steps. The pre-processing steps 106 areimplemented in a general purpose computer using software instructionsthat operate on the mass spectral data obtained in step 104. Thepre-processing steps 106 include background subtraction (step 108),normalization (step 110) and alignment (step 112). The step ofbackground subtraction preferably involves generating a robust,asymmetrical estimate of background in the spectrum and subtracts thebackground from the spectrum. Step 108 uses the background subtractiontechniques described in U.S. Pat. No. 7,736,905, which is incorporatedby reference herein. The normalization step 110 involves a normalizationof the background subtracted spectrum. The normalization can take theform of a partial ion current normalization, or a total ion currentnormalization, as described in U.S. Pat. No. 7,736,905. Step 112 alignsthe normalized, background subtracted spectrum to a predefined massscale, as described in U.S. Pat. No. 7,736,905, which can be obtainedfrom investigation of the training set used by the classifier.

Once the pre-processing steps 106 are performed, the process 100proceeds to step 114 of obtaining values of selected features (peaks) inthe spectrum over predefined m/z ranges. Using the peak-width settingsof a peak finding algorithm, the normalized and background subtractedamplitudes may be integrated over these m/z ranges and assigned thisintegrated value (i.e., the area under the curve between the width ofthe feature) to a feature. For spectra where no peak has been detectedwithin this m/z range, the integration range may be defined as theinterval around the average m/z position of this feature with a widthcorresponding to the peak width at the current m/z position. This stepis also disclosed in further detail in U.S. Pat. No. 7,736,905.

At step 114, as described in U.S. Pat. No. 7,736,905, the integratedvalues of features in the spectrum is obtained at one or more of thefollowing m/z ranges:

-   5732 to 5795-   5811 to 5875-   6398 to 6469-   11376 to 11515-   11459 to 11599-   11614 to 11756-   11687 to 11831-   11830 to 11976-   12375 to 12529-   23183 to 23525-   23279 to 23622 and-   65902 to 67502.

In a preferred embodiment, values are obtained at eight of these m/zranges shown in Table 1 below. The significance, and methods ofdiscovery of these peaks, is explained in the U.S. Pat. No. 7,736,905.

At step 116, the values obtained at step 114 are supplied to aclassifier, which in the illustrated embodiment is a K-nearest neighbor(KNN) classifier. The classifier makes use of a training set of classlabeled spectra from a multitude of other patients (which may be NSCLCcancer patients, or other solid epithelial cancer patients, e.g., HNSCC,Breast Cancer). The application of the KNN classification algorithm tothe values at 114 and the training set is explained in U.S. Pat. No.7,736,905. Other classifiers can be used, including a probabilistic KNNclassifier or other classifier. In the illustrated embodiment, thetraining set is in the form of class-labeled spectra from NSCLC patientsthat either did or did not benefit from administration of EGFRinhibitors, those that did benefit being labeled “Good” and those thatdid not labeled “Poor.”

Note that, in the illustrated embodiments of our predictive tests forbreast cancer patient treatment, the classifier uses a training set frompatients that are not breast cancer patients, but the predictions madeby the method are nevertheless valid. The reason for using the NSCLCtraining set for the present study is that it has been subject toextensive validation. However, it is certainly possible to construct atraining set and to validate it from test spectra obtained from amultitude of breast cancer patients. For example, the set of spectra weused in the EGF30008 study could be used to construct the training setand used in the classification of the test sample. Such an endeavorwould have required substantial additional validation work which was notnecessary in our case since the NSCLC training set used in theclassifier performed so well.

At step 118, the classifier produces a label for the spectrum, either“Good”, “Poor” or “Undefined”. As mentioned above, steps 104-118 areperformed in parallel on the three separate aliquots from a givenpatient sample (or whatever number of aliquots are used). At step 120, acheck is made to determine whether all the aliquots produce the sameclass label. If not, an undefined (or Indeterminate) result is returnedas indicated at step 122. If all aliquots produce the same label, thelabel is reported as indicated at step 124.

As described in this document, new and unexpected uses of the classlabel reported at step 124 are disclosed. For example, those hormonereceptor positive, HER2-negative breast cancer patients labeled “Poor”in accordance with the VeriStrat test are likely to benefit fromtreatment in the form of an endocrine therapy drug, e.g., an aromataseinhibitor (letrozole) in combination with targeted therapy (e.g.,lapatinib) in accordance with the present disclosure. As anotherexample, regardless of the HER2 status, if the patient is identified as“Poor” in accordance with the test, then the patient is not likely tobenefit from administration of an endocrine therapy drug alone.

It will be understood that steps 106, 114, 116 and 118 are typicallyperformed in a programmed general purpose computer using software codingthe pre-processing step 106, the obtaining of spectral values in step114, the application of the KNN classification algorithm in step 116 andthe generation of the class label in step 118. The training set of classlabeled spectra used in step 116 is stored in memory in the computer orin a memory accessible to the computer.

The method and programmed computer may be advantageously implemented ata laboratory test processing center as described in our prior patentapplication publication U.S. Pat. No. 7,736,905.

TABLE 1 Peaks used in VeriStrat. Peak number m/z 1 5843 2 11445 3 115294 11685 5 11759 6 11903 7 12452 8 12579

Discussion and Supporting Data

Our results from this retrospective analysis of the EGF30008 study areshown in FIGS. 2-13 and the data supporting our conclusions will bediscussed below.

FIG. 3 is a Kaplan-Meier plot of PFS for the letrozole+placebo arm ofthe EGF30008 study by VeriStrat classification. FIG. 3 demonstrates thatour VeriStrat test identifies a group of patients with poor outcomes onletrozole alone. In particular, in the letrozole+placebo arm, there wassignificant separation between VeriStrat Good and VeriStrat Poorpatients, with hazard ratio (HR)=0.36 (95% Confidence Interval (CI):0.23-0.58) and log-rank p<0.0001. The median PFS was 10.8 months forVeriStrat Good patients (n=479) and 2.8 months for VeriStrat Poorpatients (n=43).

The letrozole+lapatinib arm (not shown) showed no statisticallysignificant separation between VeriStrat Good and VeriStrat Poorpatients (log-rank p=0.53). Median PFS was 11.4 months for Good patientsand 11.0 months for Poor patients.

The effect on PFS with the addition of a targeted therapy (lapatinib) inaddition to letrozole, separated by VeriStrat classification, is shownin FIGS. 4, 5 and 6. VeriStrat “Good” patients are shown in FIG. 4 and“Poor” patients in FIG. 5. There was significant separation in bothVeriStrat classification groups in favor of letrozole+lapatinibtreatment, but the separation was much larger for “Poor” patients. For“Good” patients (FIG. 4), the HR between treatment arms was HR=0.84 (95%CI: 0.72-0.98) and log-rank p=0.028. The median PFS was 11.4 months forthe combination arm and 10.8 months for the letrozole+placebo arm.

For VeriStrat “Poor” patients (FIG. 5), HR=0.52 (95% CI: 0.31-0.86) withlog-rank p=0.011. The median PFS was 11.0 months in the combination armand only 2.8 months in the letrozole+placebo only arm.

FIG. 2 is a Kaplan-Meier plot of Progression Free Survival (PFS) foroverall population by VeriStrat classification and treatment arm. FIG. 2shows that patients have similar outcomes on the combination oflapatinib and letrozole regardless of their VeriStrat status, but not onletrozole alone. In particular, FIG. 2 shows that, for those patientstreated with letrozole alone, patients identified as “Poor” do muchworse on letrozole alone than those patients identified as “Good.” FIG.2 also shows that for patients whose serum was classified as “Poor”showed improved progression free survival (PFS) with the addition oflapatinib to letrozole.

The results of these four comparisons are summarized in Table 2.

TABLE 2 Summary of survival analysis results for PFS for the overallpopulation by treatment arm and VeriStrat classification HR P valueMedian PFS (months) VS Poor by tx 0.52 (0.31-0.86) 0.011 2.8 (let); 11.0(let + lap) VS Good by tx 0.84 (0.72-0.98) 0.028 10.8 (let); 11.4 (let +lap) Let + lat arm 0.87 (0.58-1.33) 0.53 11.0 (Poor); 11.4 (Good) by VSLet only arm 0.36 (0.23-0.58) <0.0001 2.8 (Poor); 10.8 (Good) by VS

From this data we have discovered that patients whose serum isclassified as “Poor” benefit significantly more when treated withlapatinib plus letrozole as compared to treatment with letrozole alone:progression free survival is greater by 8.2 months. The significance ofthe difference in benefit between the treatment arms was demonstrated inthe multivariate analysis that included the interaction term.

FIGS. 7 and 8 show our data of PFS for patients with known HER2 statusreceiving letrozole alone. PFS for HER2− patients is shown in FIG. 7 andPFS for HER2+ patients is shown in FIG. 8. With reference to the HER2−data (FIG. 7), in the letrozole+placebo arm, there was significantseparation between VeriStrat Good and VeriStrat Poor patients, HR=0.37(95% CI: 0.21-0.64) and log-rank p=0.0004. The median PFS was 13.6months for VeriStrat Good patients and 3.1 months for VeriStrat Poorpatients. With reference to the HER2+ data (FIG. 8), there wassignificant separation between VeriStrat Good and VeriStrat Poorpatients, HR=0.29 (95% CI: 0.09-0.98) and log-rank p=0.046. The medianPFS was 3.0 months for VeriStrat Good patients and 2.3 months forVeriStrat Poor patients.

Considering FIGS. 7 and 8 together, our data demonstrates that ourVeriStrat test identifies patients with poor outcomes on letrozole aloneindependent of HER2 status, i.e., those patients whose serum sample isclassified as “Poor” by the classifier.

Our data on the effect on PFS with the addition of lapatinib toletrozole in the HER2− population is shown in FIGS. 9 and 10. HER2−VeriStrat “Good” and “Poor” patients were analyzed separately bytreatment arm. Data for HER2−, VeriStrat “Good” patients are shown inFIG. 9 and “Poor” patients in FIG. 10. There was no significantseparation in either VeriStrat classification group. There may be atrend to separation in favor of letrozole+lapatinib treatment,especially in the VeriStrat “Poor” patients where the number of patientswas small. For “Good” patients the HR between treatment arms was 0.85(95% CI: 0.71-1.02) and log-rank p=0.085. The median PFS was 13.8 monthsfor the combination arm and 13.6 months for the letrozole+placebo arm.For VeriStrat “Poor” patients (FIG. 10), HR=0.57 (95% CI: 0.32-1.04)with log-rank p=0.068. The median PFS was 11.0 months in the combinationarm and only 3.1 months in the letrozole+placebo arm.

The results of these four comparisons are summarized in Table 3.

TABLE 3 Summary of survival analysis results for PFS for HER2− patientsby treatment arm and VeriStrat classification HR P value Median PFS(months) VS Poor by tx 0.57 (0.32-1.04) 0.068 3.1 (let); 11.0 (let +lap) VS Good by tx 0.85 (0.71-1.02) 0.085 13.6 (let); 13.8 (let + lap)Let + lat arm 0.77 (0.46-1.27) 0.30 11.0 (Poor); 13.8 (Good) by VS Letonly arm 0.37 (0.21-0.64) 0.0004 3.1 (Poor); 13.6 (Good) by VS

FIG. 6 is a Kaplan-Meier plot of Progression Free Survival (PFS) foroverall population by VeriStrat classification and treatment arm for theHER2-negative population. FIG. 6 shows that HER2− patients have similaroutcomes on the combination of lapatinib and letrozole regardless oftheir VeriStrat status, but not on letrozole alone. In particular, FIG.6 shows that, for those patients treated with letrozole alone, patientsidentified as VeriStrat Poor do much worse on letrozole alone than thosepatients identified as VeriStrat Good. FIG. 6 also shows that for thoseHER2− patients whose serum was classified as “Poor” showed a trend forimproved progression free survival with the addition of lapatinib toletrozole.

Data showing the effect on PFS with the addition of lapatinib toletrozole in the HER2 positive (HER2+) population is shown in FIGS.11-13. For HER2+ patients, each treatment arm was analyzed separately byVeriStrat classification. The data for VeriStrat Good patients is shownin FIG. 12. The data for VeriStrat Poor patients is shown in FIG. 13.The combined data for all HER2+ patients and both treatment arms isshown in FIG. 11.

In the HER2+ population, there was significant separation in bothVeriStrat classification groups in favor of letrozole+lapatinibtreatment. For “Good” patients (FIG. 12) the HR between treatment armswas 0.71 (95% CI: 0.50-0.99) and log-rank p=0.046. The median PFS was8.0 months for the combination arm and 3.0 months for the letrozole onlyarm. In “Poor” patients (FIG. 13), the separation is similar with themedian PFS of 8.6 months for the combination arm and 2.3 months for theletrozole only arm.

The results of these four comparisons are summarized in Table 4.

TABLE 4 Summary of survival analysis results for PFS for HER2+ patientsby treatment arm and VeriStrat (VS) classification HR P value Median PFS(months) VS Poor by tx 0.17 (0.04-0.76) 0.021 2.3 (let); 8.6 (let + lap)VS Good by tx 0.71 (0.50-0.99) 0.046 3.0 (let); 8.0 (let + lap) Let +lat arm 0.99 (0.40-2.48) 0.99 8.6 (Poor); 8.0 (Good) by VS Let only arm0.29 (0.09-0.98) 0.046 2.3 (Poor); 3.0 (Good) by VS

FIGS. 11, 12 and 13 show that HER2+ patients have similar outcomes withlapatinib plus letrozole treatment regardless of VeriStratclassification.

These results taken together indicate that the VeriStrat Poor patientsbenefit from the addition of lapatinib to letrozole independently fromtheir HER2 status.

PFS Interaction Analysis

A Cox Proportional Hazard Model analysis was carried out includingVeriStrat classification, treatment arm, and an interaction term betweenthe two. The results are shown in Table 5. Treatment and VeriStratclassification were both significant, as was the interaction term,indicating that the difference in Hazard Ratio (HR) between VeriStratGood and VeriStrat Poor patients is significantly different between theletrozole+placebo arm and the letrozole+lapatinib arm.

TABLE 5 Results of the Cox Proportional Hazards Model with CovariateSelection HR 95% CI P value VeriStrat Classification 0.41 0.29-0.58<0.0001 (good vs poor) Treatment Arm 0.36 0.22-0.60 <0.0001 (Let + Lapvs Let) HER2 Status 1.76 1.46-2.13 <0.0001 HER2+ vs HER2− ECOG PS (>1 vs0) 1.43 1.23-1.66 <0.0001 Prior adjuvant hormonal 0.54 0.45-0.65 <0.0001therapy (<6 months vs >6 months) Hormone receptor status 1.48 1.01-2.180.046 (ER− and PgR− vs ER+ and/or PgR+) No. of metastatic sites (≧3 1.561.34-1.81 0.0001 vs <3) Veristrat*treatment 2.31 1.36-3.94 0.0020interaction

While the EGF30008 study involved a single targeted therapy (lapatinib)and a single aromatase inhibitor (letrozole), there are several dualHER2 and EGFR inhibitors under investigations, e.g. neratinib, afatinib,ARRY-543 that are likely examples of other targeted therapies that couldbe used in the method. Also, the effect of the combination of EGFRinhibitors (erlotinib, gefitinib) and HER2 inhibitor (trastuzumab) maybe similar to one of the dual inhibitors.

Letrozole belongs to the class of selective reversible aromataseinhibitors, as well as Anastrozole (Arimidex); another similar acting,however non-reversible, agent is Exemestane (Aromasin). The methods ofthis disclosure may be used to predict HER2−, post-menopausal hormonereceptor positive breast cancer patient benefit from the combination oftargeted therapies and an aromatase inhibitor other than letrozole.

Definitions:

As used herein, the terms “endocrine therapy” and “endocrine therapydrugs” should be interpreted to mean those drugs which influence theendocrine system by modulating estrogen synthesis and/or estrogenreceptor pathways, including but not limited to SERDs, SERMs andaromatase inhibitors.

The term “targeted therapies” should be interpreted to mean those drugstargeting specific pathways within the cell, including but not limitedto EGFR-Is, HER2 inhibitors, lapatinib and combinations thereof.

The term “hormone receptor positive” is intended to include estrogen(ER) and/or progesterone (PgR) receptors-positive breast cancerpatients.

All questions concerning the scope of the invention are to be answeredby reference to the appended claims.

REFERENCES

-   1. Goldhirsch, A., M. Colleoni, and R. D. Gelber, Endocrine therapy    of breast cancer. Ann Oncol, 2002. 13 Suppl 4: p. 61-8.-   2. Ma, C. X., C. G. Sanchez, and M. J. Ellis, Predicting endocrine    therapy responsiveness in breast cancer. Oncology (Williston    Park), 2009. 23(2): p. 133-42.-   3. Johnston, S., et al., Lapatinib combined with letrozole versus    letrozole and placebo as first-line therapy for postmenopausal    hormone receptor-positive metastatic breast cancer. J Clin    Oncol, 2009. 27(33): p. 5538-46.-   4. Chlebowski, R. T., Optimizing aromatase inhibitor integration    into initial treatment strategies in postmenopausal women with    hormone-receptor-positive early breast cancer. Breast Cancer Res    Treat, 2008. 112 Suppl 1: p. 25-34.-   5. Seruga, B. and I. F. Tannock, Up-front use of aromatase    inhibitors as adjuvant therapy for breast cancer: the emperor has no    clothes. J Clin Oncol, 2009. 27(6): p. 840-2.-   6. Dowsett, M., et al., Meta-analysis of breast cancer outcomes in    adjuvant trials of aromatase inhibitors versus tamoxifen. J Clin    Oncol, 2010. 28(3): p. 509-18.-   7. Cuzick, J., The ATAC trial: the vanguard trial for use of    aromatase inhibitors in early breast cancer. Expert Rev Anticancer    Ther, 2007. 7(8): p. 1089-94.-   8. Tobias, J. S. and A. Howell, An open randomised trial of    second-line endocrine therapy in advanced breast cancer: comparison    of the Aromatase inhibitors letrozole and anastrozole. Eur J    Cancer, 2004. 40(12): p. 1913.

1. A method of determining whether a hormone receptor positive breastcancer patient, regardless of HER2 status, is unlikely to benefit fromadministration of an endocrine therapy drug alone for treatment of thecancer, comprising the steps of: a) obtaining a mass spectrum from ablood-based sample from the patient; b) performing one or morepredefined pre-processing steps on the mass spectrum obtained in stepa); c) obtaining values of selected features in said spectrum at one ormore predefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed; d) using the values obtained instep c) in a classification algorithm using a training set comprisingclass-labeled spectra produced from samples from other cancer patientsand obtaining a class label for the sample; and e) if the class labelobtained in step d) is “Poor” or the equivalent, then the patient isidentified as being unlikely to benefit from the endocrine therapy drug.2. The method of claim 1, wherein the endocrine therapy drug comprises aselective estrogen receptor modulator (SERM).
 3. The method of claim 1,wherein the endocrine therapy drug comprises selective estrogen receptordownregulator (SERD).
 4. The method of claim 2, wherein the endocrinetherapy drug comprises tamoxifen or the equivalent.
 5. The method ofclaim 1, wherein the endocrine therapy drug comprises an aromataseinhibitor.
 6. The method of claim 5, wherein the aromatase inhibitorcomprises letrozole or the equivalent.
 7. The method of claim 1, whereinthe one or more m/z ranges comprises one or more m/z ranges selectedfrom the group of m/z ranges consisting of: 5732 to 5795 5811 to 58756398 to 6469 11376 to 11515 11459 to 11599 11614 to 11756 11687 to 1183111830 to 11976 12375 to 12529 23183 to 23525 23279 to 23622 and 65902 to67502.
 8. The method of claim 1, wherein the class-labeled spectra fromother cancer patients used in the classification step d) compriseclass-labeled spectra of samples obtained from non-small cell lungcancer patients and the class labels indicate whether such patientsbenefitted from treatment with an epidermal growth factor receptorinhibitor (“Good”) or did not benefit (“Poor”).
 9. A method ofdetermining whether a post-menopausal hormone receptor positive breastcancer patient with HER2-negative status is likely to benefit fromadministration of a combination treatment comprising administration of atargeted anti-cancer drug in addition to an endocrine therapy drug,comprising the steps of: a) obtaining a mass spectrum from a blood-basedsample from the patient; b) performing one or more predefinedpre-processing steps on the mass spectrum obtained in step a); c)obtaining values of selected features in said spectrum at one or morepredefined m/z ranges after the pre-processing steps on the massspectrum in step b) have been performed; and d) using the valuesobtained in step c) in a classification algorithm using a training setcomprising class-labeled spectra produced from samples from other cancerpatients and obtaining a class label for the sample; and e) if the classlabel obtained in step d) is “Poor” or the equivalent then the patientis identified as being likely to benefit from the combination treatment.10. The method of claim 9, wherein the targeted anti-cancer drugcomprises lapatinib.
 11. The method of claim 9, wherein the endocrinetherapy drug comprises an aromatase inhibitor.
 12. The method of claim11, wherein the aromatase inhibitor comprises letrozole.
 13. The methodof claim 9, wherein the endocrine therapy drug comprises a selectiveestrogen receptor modulator (SERM).
 14. The method of claim 9, whereinthe endocrine therapy drug comprises selective estrogen receptordownregulator (SERD).
 15. The method of claim 13, wherein the endocrinetherapy drug comprises tamoxifen or the equivalent.
 16. The method ofclaim 9, wherein the one or more m/z ranges comprises one or more m/zranges selected from the group of m/z ranges consisting of: 5732 to 57955811 to 5875 6398 to 6469 11376 to 11515 11459 to 11599 11614 to 1175611687 to 11831 11830 to 11976 12375 to 12529 23183 to 23525 23279 to23622 and 65902 to
 67502. 17. The method of claim 9, wherein theclass-labeled spectra from other cancer patients used in theclassification step d) comprise class-labeled spectra of samplesobtained from non-small cell lung cancer patients and the class labelsindicate whether such patients were likely to benefit from treatmentwith an epidermal growth factor receptor inhibitor (“Good”) or did notbenefit (“Poor”).